Peukert-Informed Prediction and Screening of Manganese-Based High-Rate Potassium Cathode

Manganese-based layered oxides have emerged as promising cathode materials for potassium-ion batteries owing to their low cost, environmental benignity, structural diversity, and high theoretical capacity, yet suffer from poor rate performance. Compositional regulation offers an effective strategy to address this limitation, but traditional trial-and-error approaches are inefficient and often yield suboptimal results. Herein, interpretable machine learning combining Random Forest, high-throughput virtual screening, and Pareto-front optimization of capacities at 50 and 500 mA g^-1 is employed to identify promising Mn-based layered cathodes. Two complementary parameters, Peukert capacity and Peukert-derived rate-decay descriptor, offer insight into capacity and rate-dependent behavior. Screening 14,558 candidate compositions derived from 22 dopant elements reveals compositional regions that balance practical capacity and rate performance. The model-recommended composition delivers approximately 67 mAh g^-1 at 500 mA g^-1 and retains over 70% of its initial capacity after 500 cycles. This work demonstrates an interpretable and applicability-aware methodological framework for Pareto-guided screening of high-rate cathodes, providing generalizable insights for accelerating the discovery of advanced energy-storage materials.